.png){kind=icon}
.png){kind=icon}
Return
How to systematically and quantifiably remove meaning?
.png){kind=icon}
OA
AI
DOI：10.3389/frai.2026.1783410.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Large language models increasingly mediate real-world tasks; yet we lack systematic ways to quantify how their performance degrades when the meaning of their inputs is eroded. To bridge this gap; we developed a framework to semantically erode meaning and quantify its intensity; grounded in discourse analysis; psycholinguistics; and software engineering; comprising five theoretically motivated methods: omission of key information and context; lexical substitution with near-synonyms; increased abstraction; structural obfuscation and renaming; and injection of logical errors. We applied these erosion operators across five domains and quantified their effects on model performance using a publicly available language model. A two-way Analysis of Variance (ANOVA) revealed significant main effects of both domain and erosion method; as well as a significant interaction; indicating that the impact of semantic degradation depends jointly on how text is eroded and how domain-specific information is encoded. Logical error erosions proved especially damaging for code generation; whereas structural obfuscation most strongly impaired news and instruction tasks. Epistasis analysis of pairwise erosion unions showed that some combinations produced super-additive degradation while others exhibited compensatory effects. These domain-by-erosion profiles provide diagnostic insight into where multi-step large language model (LLM) pipelines are most likely to fail and suggest that robustness benchmarks should probe models along domain-specific vulnerability dimensions rather than relying on generic perturbations. Semantic erosion thus offers a principled tool for turning model failure into evidence about how language models structure and degrade meaning.
Keywords:
large language models
meaning and semantics
meaning degradation
robustness evaluation
semantic erosion
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.